Sovereign bond yield connectedness among major economies during turmoil
Bibliographic record
Abstract
Purpose This research evaluates yield connectedness dynamics between sovereign bonds among the G7 and larger economies such as China, Russia and India, encompassing the pandemic and the Russia–Ukraine war. Design/methodology/approach The study collated daily data on sovereign bond yields from January 2011 to November 2023. The data were divided into three subsamples: pre-COVID, COVID-19 and Russia–Ukraine war periods. The Diebold and Yilmaz connectedness approach with the time-varying parameter vector autoregression (TVP-VAR) model is applied to investigate the connectedness among the countries. Findings Germany, the United States, Canada and the UK were the major transmitters, with Germany and the US as the prime net transmitters. Japan, India and Italy were net receivers. Japan consistently receives net spillovers from Canada, Germany and the USA, while transmitting to the UK. Italy mainly receives from Germany and France, while China transmits to the UK, France, Germany and the USA. The UK receives from China and Russia, and India primarily from the USA and France. Research limitations/implications COVID-19 highlighted the stabilizing role of monetary and fiscal policies, particularly in Germany and India. Major economies’ interconnectedness emphasizes the need for diversified risk management and international cooperation to maintain sovereign bond market stability. Originality/value The study examines the impact of COVID-19 and the war on global financial markets, focusing on sovereign bond yield connectedness, identifying influential economies and offering insights for financial stability enhancement.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".